Tag women in science

AMCIS2026 Human–AI Collaboration and Governance for Responsible and Sustainable Digital Ecosystems mini-track

Anastasija Nikiforova Leave a comment

As digital transformation accelerates, the convergence of AI, data governance, and ecosystem thinking is reshaping how organizations create strategic value, build competitiveness, and sustain innovation advantage. Digital and data ecosystems are increasingly complex, spanning cloud, edge, and decentralized architectures such as data meshes and lakehouses, raising critical questions of trustworthiness, responsibility, and sustainability in AI integration.

This AMCIS2026 mini-track (by Association of Information Systems (AIS)) explores how AI, including increasingly agentic systems, acts as both a strategic enabler and active participant in digital and data ecosystems, enhancing governance, augmenting and automating decision-making, and transforming how organizations create value, while raising important governance, ethical, and human-agency considerations. We invite research examining how these ecosystems can remain responsible, resilient, and sustainable, while enhancing organizational agility, competitiveness, and long-term strategic performance across sectors such as government, healthcare, finance, manufacturing, and education.

The track bridges perspectives from information systems, data science, AI governance, and sustainability research to understand how the strategic and responsible design and management of AI-driven data ecosystems can support long-term value creation, competitiveness, and societal transformation. We invite interdisciplinary contributions from fields such as computer science, management science, data science, process science, decision science, organizational design, policy-making, complexity, behavioral economics, and the social sciences. Submissions may include conceptual, design science, empirical, theoretical, or case-based studies, including literature reviews.

Topics of interest include but are not limited to:

  • AI for governance, accountability, and trustworthiness in digital and data ecosystems;
  • human–AI collaboration and delegation, human-in-the-loop and hybrid governance;
  • responsible, sustainable, and strategically aligned management of AI-augmented data ecosystems, including Green AI;
  • governance and data management in emerging architectures (e.g., data mesh, data lakehouse), including data quality, transparency, and explainability;
  • transition from centralized to decentralized data architectures – organizational and design challenges;
  • ethical, interoperable, observable, and explainable AI in connected and cross-sectoral data ecosystems;
  • co-evolution of digital and data ecosystem components;
  • coopetition between digital and data ecosystems;
  • resilience, sustainability, and long-term governance of digital infrastructures;
  • socio-technical, organizational, and policy approaches to trustworthy and responsible data ecosystems;
  • emerging technologies (e.g., blockchain, edge computing, generative AI, digital twins, IoT, AR/VR) shaping responsible, sustainable, and energy- or resource-efficient strategic ecosystem innovation;
  • empirical studies and sectoral case analyses (e.g., healthcare, finance, government, education) on evolving AI-driven ecosystems;
  • design science, conceptual, and interdisciplinary frameworks for responsible, sustainable, and strategically effective data ecosystem innovation.

This mini-track will serve as a platform for interdisciplinary dialogue on the critical role of responsible, sustainable, and strategically oriented digital and data ecosystems in driving competitive and societal innovation. Researchers and practitioners are invited to share insights, theoretical perspectives, and empirical findings in this rapidly evolving domain.

📌 Submission Deadline: March 1, 2026
📍 Venue: AMCIS 2026 — Reno, Nevada (August 20–22)

Mini-Track Chairs

Anastasija Nikiforova – University of Tartu, Estonia
Daniel Staegemann – Otto von Guericke University Magdeburg, Germany
Asif Gill – University of Technology Sydney, Australia
Martin Lnenicka – University of Hradec Králové, Czech Republic
George Marakas – Florida International University, USA

Read more and submit papers via AMCIS2026 website.

PhD Opportunity: Responsible & Sustainable AI, University of Tartu

Anastasija Nikiforova Leave a comment

We are planning to open fully funded 4-year PhD position(s) starting in 2026/2027 (University of Tartu · Information Systems Research Group), seeking ambitious, motivated candidates to explore the frontier of Responsible and Sustainable AI. To this end, we open the call for Expression of Interest.

This PhD program is ideal for those interested in:
🔹 Sustainable / Green AI & AI lifecycle sustainability
🔹 Organisational capability development for Sustainable AI
🔹 Responsible AI adoption & governance in organisational or public sector contexts
🔹 AI for trust, transparency, and organisational impact

Illustrative example topics include but are not limited to Sustainable / Green AI capability models, Green AI lifecycle & data ecosystem sustainability, Responsible GenAI adoption & governance, AI-enabled ESG reporting and impact assurance. Candidates, however, are welcome and are even encouraged to propose their own research angle within the scope of Responsible or Sustainable AI.

Who can apply?

  • Backgrounds: Information Systems, Digital Government, Sustainability, Management, Computer Science, Public Administration, or related fields
  • Experience with qualitative/quantitative research, mixed methods, or Design Science Research is a plus
  • Strong motivation and interest can compensate for limited experience

📄 How to express interest? Send a brief note including:

  1. CV
  2. 1–2 page research idea
  3. Transcript of records
  4. (Optional) Writing sample

📬 Email: anastasija.nikiforova@ut.ee. Subject line: EOI — PhD in Responsible/Sustainable AI and Information Systems

Why express interest now?

  • This is pre-application, not the formal call, but can strengthen your chances in a competitive pool (50+ applications per position).
  • Selected candidates may receive guidance on refining research ideas and preparing a strong application aligned with the Information Systems focus.

🌟 Join us at the University of Tartu Institute of Computer Science, among the top 1% of globally cited universities, to shape the future of responsible, sustainable, and trustworthy AI.

Green AI: ENFIELD Challenge & ECAI2025 Workshop

Anastasija Nikiforova Leave a comment

🔴🟢 Red or Green Pill? Which path will AI take?

Last month, we officially launched the Green AI Challenge, part of the Horizon Europe ENFIELD – AI Network of Excellence, and October saw a major step forward during an intensive research visit to the Know Center in Graz (Austria) —a hub of AI research and cross-sector collaboration. Working alongside Nicki Lisa Cole we advanced the conceptual and methodological foundations of our Green AI initiative, merging complementary perspectives into a promising framework for sustainable AI.

Why Green AI matters

AI is accelerating rapidly, but so are its environmental and societal side-effects – rising compute demands, energy-intensive models, and the broader ecological footprint of scaling AI. Policies, incentives, and institutional capacity are often lagging behind, creating a gap between AI adoption and responsible, sustainable practice.

The Green AI Challenge aims to understand how organizations across Europe approach Green AI, identifying struggles, gaps, and opportunities, and ultimately co-creating a validated framework for adoption that informs both policy and governance.

Community Call

We need insights from anyone working in AI, sustainability, digital transformation, public policy, or tech governance:

  • What perspectives on Green AI feel most critical today?
  • Where are the biggest gaps, risks, or untapped opportunities?
  • Examples of Green AI in practice (good or bad)?
  • Recommendations for interviews, readings, or collaborators?
  • Frameworks, metrics, or research to guide our work?

Your input will directly shape policy recommendations and adoption frameworks for a more sustainable, trustworthy AI future. Comment below or message us to contribute. Let’s choose the Green Pill together, as we also did with those who joined us earlier in October as part of ECAI2025 Green AI workshop.

Earlier last month, as part of the 28th European Conference on Artificial Intelligence (ECAI2025) in Bologna, together with Riccardo Cantini, Luca Ferragina, Davide Mario Longo, Simona Nisticò, Francesco Scarcello, Reza Shahbazian, Dipanwita Thakur, Irina Trubitsyna, and Giovanna Varricchio, we organized a workshop on environmentally responsible AI, where across three thematic tracks of Sustainability, Green AI, and Applications, 17 talks exploring pathways toward sustainable AI practice were delivered, with the special highlight of keynote talk delivered by Thomas Eiter on “The Bilateral AI approach for Green and Sustainable AI,” introducing a framework that integrates symbolic and subsymbolic methods to advance more efficient and Ecologically Responsible AI.

🌱 All in all, the path forward is ours to shape. By working together—researchers, policymakers, and practitioners—we can turn Green AI from a vision into practice. Let’s continue this journey being committed to sustainable, responsible AI. Let’s choose the Green Pill, together. 🟢

From “Data Quality for AI” to “AI for Data Quality Management”: Insights from BIR 2025

Anastasija Nikiforova Leave a comment

Back in September, our paper “From Data Quality for AI to AI for Data Quality: A Systematic Review of Tools for AI-Augmented Data Quality Management in Data Warehouses” was presented at the BIR Business Informatics Research Conference (BIR) in my hometown, Riga.

The paper, authored by my former student Heidi Carolina Tamm (now DW Lead Designer at Swedbank Estonia) and myself, explores a central question: can AI do more than just consume high-quality data—can it actively create and maintain it?

From “Data Quality for AI” to “Data Quality for AI & AI for Data Quality Management”

High-quality data underpins analytics, AI performance, and regulatory compliance, yet DQ management remains complex, resource-intensive, and often manual. With global data projected to reach 175 zettabytes by 2025, poor DQ carries real business costs—up to 19% of companies report customer loss due to inaccurate or incomplete data.

Traditionally, data quality (DQ) management focuses on ensuring AI has reliable, clean data. We propose a complementary perspective: using AI itself to enhance and automate data quality management. This is particularly relevant for enterprise data warehouses (DWs), which remain central despite the growth of decentralized and domain-driven architectures.

To this end, we systematically reviewed 151 DQ tools, evaluating their support for AI-augmented DQM, particularly automation of rule detection and anomaly identification. Key findings:

  • only 10 tools fully support AI-augmented DQM in data warehouses;
  • most tools focus on data cleansing rather than leveraging AI for rule discovery or explainable quality improvements;
  • metadata + rule-based + ML hybrid approaches show promise but remain underutilized;
  • SQL and natural language rule definitions are rarely supported, yet essential for practical use;
  • explainable AI and governance features are critical for trust;
  • cloud scalability with GDPR compliance is a must for enterprise adoption.

For organizations, AI-driven DQM goes beyond cleaning and profiling, enabling detection, enforcement, and explanation of data quality issues. This leads to better compliance, efficiency, and trust in enterprise data ecosystems.

For researchers and tool developers, there’s a clear need for next-generation DQ tools that are explainable, support SQL/natural language rules, integrate reconciliation logic, and operate under real-world regulatory and architectural constraints. LLMs and AI models can play a role, though they are not a silver bullet; early research shows strong potential.

Our study highlights that while AI has mainly been seen as a consumer of data quality, there is an untapped opportunity to use AI to actively improve DQ, shifting from “Data Quality for AI” to “Data Quality for AI and AI for Data Quality Management.” This research provides practical guidance for organizations and sets the stage for next-generation AI-augmented data governance and DQM solutions.

Read more in the article.

AI, Data, and Public Benefit: Reflections from Data for Policy CIC 2025

Anastasija Nikiforova Leave a comment

At Data for Policy CIC 2025 in The Hague, themed “Twin Transition in Data and Policy for a Sustainable and Inclusive Future,” we explored how emerging technologies—especially AI—and diverse data sources can support accountable, inclusive, and sustainable policymaking.

Across several full days of sessions, the event brought together transdisciplinary researchers, policy practitioners, and technology experts. Notable contributions included:

  1. Research presentations – “Proactive-by-Design: The Future of Governance Beyond Bureaucracy?,” on the study we conduct with Paula Rodriguez Müller & Luca Tangi (European Commission JRC) and “The Data Agency Awakens: A New Era for Official Statistics” we prepared with Luca Di Gennaro Splendore
  2. Panel on National Data Strategies in Europe: Learning from and for the Dutch Data Strategy, which I joined the conversation as a panelists, along with Tim Faber (Ministry of Interior & Kingdom Relations), Anne Fleur van Veenstra, Iryna Susha, Adrianna Michałowicz (chaired by Devin Diran & Thijmen van Gend)
  3. finally, the paanel on Responsible AI and Data Science Revolutions: Implications for Public Benefit Research and Policy Making that I was honored to chair, with panelists representing Smart Data Foundry and the Joseph Rowntree Foundation (JRF), joined by Magdalena Getler, Oliver Berry, and Rosario Piazza.

Our discussion highlighted the immense potential of AI and data, but also the responsibilities that come with it. Key insights included:

  • Balancing optimism and realism: AI is transformative, but its adoption requires grounded, practical experience. It can support public benefit—if managed carefully;
  • Data quality over quantity: More data isn’t always better. Governance, documentation, bias mitigation, and transparency are essential for AI-readiness;
  • Embedding public trust: High-sensitivity contexts, such as health or finance, demand proportionality, oversight, and systems designed for inclusivity;
  • Human-in-the-loop mechanisms: Ensuring AI reflects human values and context is critical, even when those values are evolving;
  • Task-appropriate AI: Not every problem requires a large language model; careful alignment between technology and purpose is essential;
  • Data literacy for all: Understanding AI’s limitations and risks is as important as technical infrastructure;
  • Triangulating perspectives: Combining structured/unstructured and qualitative/quantitative data helps address bias, power imbalances, and complexity;
  • Sustainable and inclusive systems: Scalable infrastructure alone is insufficient; AI governance and operational design must prioritize long-term societal benefit.

As Amara’s Law reminds us: “We tend to overestimate the effect of a technology in the short run and underestimate it in the long run.” Our discussion echoed this sentiment: AI’s potential is enormous, but realizing it for public benefit requires careful design, governance, and collaboration.

The overarching theme through all days (beyond above) was clear: AI and data are not neutral tools. Their value for public policy depends on human-centered design, responsible governance, and active societal engagement. Tools alone won’t deliver public benefit—they must be operationalized thoughtfully, with attention to ethics, context, and inclusion.

Huge thanks to Sarah Giest, Bram Klievink, Zeynep Engin, and all participating institutions—Leiden University, TNO Vector, Cambridge University Press & Assessment, and The Hague Centre for Digital Governance—for creating the space for meaningful dialogue in such a rich, collaborative environment.

The CIC 2025 conversations reminded us: building a truly responsible, inclusive, resilient, and sustainable AI ecosystem is not just a technical challenge—it is a societal mission and each and every of us is part of it.